import os

import torch
from torch.autograd import Function
from torch.nn import functional as F
from torch.utils.cpp_extension import load


os.system("unset TORCH_CUDA_ARCH_LIST")


module_path = os.path.dirname(__file__)


def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
    if input.device.type == "cpu":
        out = upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])

    else:
        upfirdn2d_op = load(
            "upfirdn2d",
            sources=[
                os.path.join(module_path, "upfirdn2d.cpp"),
                os.path.join(module_path, "upfirdn2d_kernel.cu"),
            ],
        )

        class UpFirDn2dBackward(Function):
            @staticmethod
            def forward(ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size):
                up_x, up_y = up
                down_x, down_y = down
                g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad

                grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)

                grad_input = upfirdn2d_op.upfirdn2d(
                    grad_output,
                    grad_kernel,
                    down_x,
                    down_y,
                    up_x,
                    up_y,
                    g_pad_x0,
                    g_pad_x1,
                    g_pad_y0,
                    g_pad_y1,
                )
                grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])

                ctx.save_for_backward(kernel)

                pad_x0, pad_x1, pad_y0, pad_y1 = pad

                ctx.up_x = up_x
                ctx.up_y = up_y
                ctx.down_x = down_x
                ctx.down_y = down_y
                ctx.pad_x0 = pad_x0
                ctx.pad_x1 = pad_x1
                ctx.pad_y0 = pad_y0
                ctx.pad_y1 = pad_y1
                ctx.in_size = in_size
                ctx.out_size = out_size

                return grad_input

            @staticmethod
            def backward(ctx, gradgrad_input):
                (kernel,) = ctx.saved_tensors

                gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)

                gradgrad_out = upfirdn2d_op.upfirdn2d(
                    gradgrad_input,
                    kernel,
                    ctx.up_x,
                    ctx.up_y,
                    ctx.down_x,
                    ctx.down_y,
                    ctx.pad_x0,
                    ctx.pad_x1,
                    ctx.pad_y0,
                    ctx.pad_y1,
                )
                # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0], ctx.out_size[1], ctx.in_size[3])
                gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1])

                return gradgrad_out, None, None, None, None, None, None, None, None

        class UpFirDn2d(Function):
            @staticmethod
            def forward(ctx, input, kernel, up, down, pad):
                up_x, up_y = up
                down_x, down_y = down
                pad_x0, pad_x1, pad_y0, pad_y1 = pad

                kernel_h, kernel_w = kernel.shape
                batch, channel, in_h, in_w = input.shape
                ctx.in_size = input.shape

                input = input.reshape(-1, in_h, in_w, 1)

                ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))

                out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
                out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
                ctx.out_size = (out_h, out_w)

                ctx.up = (up_x, up_y)
                ctx.down = (down_x, down_y)
                ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)

                g_pad_x0 = kernel_w - pad_x0 - 1
                g_pad_y0 = kernel_h - pad_y0 - 1
                g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
                g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1

                ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)

                out = upfirdn2d_op.upfirdn2d(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1)
                # out = out.view(major, out_h, out_w, minor)
                out = out.view(-1, channel, out_h, out_w)

                return out

            @staticmethod
            def backward(ctx, grad_output):
                kernel, grad_kernel = ctx.saved_tensors

                grad_input = UpFirDn2dBackward.apply(
                    grad_output,
                    kernel,
                    grad_kernel,
                    ctx.up,
                    ctx.down,
                    ctx.pad,
                    ctx.g_pad,
                    ctx.in_size,
                    ctx.out_size,
                )

                return grad_input, None, None, None, None

        out = UpFirDn2d.apply(input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1]))

    return out


def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
    _, channel, in_h, in_w = input.shape
    input = input.reshape(-1, in_h, in_w, 1)

    _, in_h, in_w, minor = input.shape
    kernel_h, kernel_w = kernel.shape

    out = input.view(-1, in_h, 1, in_w, 1, minor)
    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
    out = out.view(-1, in_h * up_y, in_w * up_x, minor)

    out = F.pad(out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
    out = out[
        :,
        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
        :,
    ]

    out = out.permute(0, 3, 1, 2)
    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
    out = F.conv2d(out, w)
    out = out.reshape(
        -1,
        minor,
        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
    )
    out = out.permute(0, 2, 3, 1)
    out = out[:, ::down_y, ::down_x, :]

    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1

    return out.view(-1, channel, out_h, out_w)
